TopoSZp: Lightweight Topology-Aware Error-controlled Compression for Scientific Data

TL;DR

TopoSZp is a lightweight, topology-aware lossy compression method that efficiently preserves critical topological features in scientific data, significantly reducing computational overhead while maintaining high compression quality.

Contribution

It introduces a novel topology-aware compressor built on SZp that efficiently preserves critical points with minimal overhead and strict error bounds.

Findings

Achieves 3 to 100 times fewer non-preserved critical points.

Provides 100 to 10,000 times faster compression and 10 to 500 times faster decompression.

Maintains competitive compression ratios with no false positives.

Abstract

Error-bounded lossy compression is essential for managing the massive data volumes produced by large-scale HPC simulations. While state-of-the-art compressors such as SZ and ZFP provide strong numerical error guarantees, they often fail to preserve topological structures (example, minima, maxima, and saddle points) that are critical for scientific analysis. Existing topology-aware compressors address this limitation but incur substantial computational overhead. We present TopoSZp, a lightweight, topology-aware, error-controlled lossy compressor that preserves critical points and their relationships while maintaining high compression and decompression performance. Built on the high-throughput SZp compressor, TopoSZp integrates efficient critical point detection, local ordering preservation, and targeted saddle point refinement, all within a relaxed but strictly enforced error bound. Experimental results on real-world scientific datasets show that TopoSZp achieves 3 to 100 times fewer non-preserved critical points, introduces no false positives or incorrect critical point types, and delivers 100 to 10000 times faster compression and 10 to 500 times faster decompression compared to existing topology-aware compressors, while maintaining competitive compression ratios.